電影票價 (2026-01)
| 影城 | 全票 | 早場 |
|---|---|---|
| 台北信義威秀 | 360 | 300 |
| 台北南港LaLaport威秀 | 360 | 300 |
| 台北京站威秀 | 360 | 300 |
| 台北西門威秀 | 320 | 260 |
| 新店裕隆城威秀 | 350 | 300 |
| 板橋大遠百威秀 | 350 | 300 |
| VibeCoded AI-Slop License v1.0 | |
| Copyright (c) [YEAR] [AUTHOR] | |
| This software, source code, documentation, configuration, generated artifacts, | |
| and whatever else happened to end up in this repository (collectively, the | |
| "Software") is released under the VibeCoded AI-Slop License v1.0. | |
| 1. Permission |
電影票價 (2026-01)
| 影城 | 全票 | 早場 |
|---|---|---|
| 台北信義威秀 | 360 | 300 |
| 台北南港LaLaport威秀 | 360 | 300 |
| 台北京站威秀 | 360 | 300 |
| 台北西門威秀 | 320 | 260 |
| 新店裕隆城威秀 | 350 | 300 |
| 板橋大遠百威秀 | 350 | 300 |
| #include <stdio.h> | |
| #include <stdlib.h> | |
| #include <libproc.h> | |
| // Uses proc_pidinfo from libproc.h to find the parent of given pid. | |
| // Call this repeatedly until ppid(pid) == pid to get ancestors. | |
| int ppid(pid_t pid) { | |
| struct proc_bsdinfo info; | |
| proc_pidinfo(pid, PROC_PIDTBSDINFO, 0, &info, sizeof(info)); | |
| return info.pbi_ppid; |
| =============================================================================== | |
| User Name-: Hardik | |
| Registration code :- TZXZT-USMCB-ZRKYP-MTVG3-JM8UL | |
| =============================================================================== |
This is my OpenCode setup for local models, mainly using a customized llama.cpp configuration.
For Qwen 3.8, DeepSeek V4, and Glimmer, the models are already trained to support reasoning effort levels. Depending on the model, these may be exposed as low, medium, high, xhigh, or as low, high, and max.
For models that support reasoning but were not trained with explicit reasoning-effort levels, such as the Qwen 3.5 and 3.6 variants, I use a token budget to limit the amount of reasoning.
Although Qwen 3.8 has built-in reasoning-effort levels, I still apply a maximum reasoning-token cap for each effort level.
Although the llama.cpp CLI flags specify preserve_thinking and a default reasoning budget, these can still be overridden through the API, so this works fine for my setup.
| #r "nuget: FSharp.Compiler.Service, 43.8.300" | |
| open FSharp.Compiler.Syntax | |
| open FSharp.Compiler.SyntaxTrivia | |
| open FSharp.Compiler.Xml | |
| open FSharp.Compiler.CodeAnalysis | |
| open System.IO | |
| type Node = | |
| { Data : Data |
| Create a production-ready, visually stunning **front-end web app**. | |
| GOAL | |
| Build a single-page application (plus detail routes) for a fictional company: | |
| **"SILICON EXCHANGE"** — a marketplace where people rent out idle GPUs and AI | |
| accelerators by the hour. Renters browse listings, inspect live utilization | |
| charts, and reserve time blocks. | |
| FRONT END ONLY. No backend, no database, no auth server, no API keys. All data | |
| is mock data defined in code. But the app must behave like the real thing — the |
https://code-boxx.com/simple-php-webauthn/
$rp to your own in 3-init.php.composer require lbuchs/webauthn to download the PHP Web Authn LibraryCopyright by Code Boxx
| 2teams() { | |
| NOW=$(date +"%Y-%m-%d_%H%M") | |
| if [ ! -z $2 ] ; then | |
| echo $NOW"_"$2.mp4 | |
| ffmpeg -i $1 -codec copy $NOW"_"$2.mp4 | |
| else | |
| echo $NOW"_teamsvid".mp4 | |
| ffmpeg -i $1 -codec copy $NOW"_teamsvideo".mp4 | |
| fi | |
| } |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.